Author

Date

4 April 2023

Intern Post

The dingo is among Australia’s most recognised species internationally. However, debate continues about whether dingoes are considered invasive or native species.

Dingoes arrived in Australia around 3,000–5,000 years ago, and their rapid disperal was likely facilitated by humans.

In the present day, dingoes have major negative impacts on livestock producers, especially sheep farmers. To reduce these impacts, landowners and government spend an estimated ~$30 million annually across Australia to control wild dog and dingo populations. Populations are controlled using traps, baits and shooting, along with a barrier to stop their movement into unwanted areas: the Dingo Fence.

The Dingo Fence is a wire fence made to protect from the loss of sheep for the sheep industry; it is the longest fence in the world (5,614 km). Since its construction 80 years ago, the Dingo Fence has shaped the landscape of Australia. However, perhaps unintentionally, the dingo fence has acted as a natural experiment. On the side where dingoes remain present there is more vegetation and fewer invasive species like foxes than on the side where dingoes are absent. The difference between sides of the fence is so distinct that you can even see it from space! Nearly a century later, the dingo fence has shown the importance of apex predators like dingoes in ecosystems and their benefit to native biodiversity.

To understand the debate about dingo’s role in Australia’s ecosystems, it’s useful to know its distribution. Here, we’ll explore how historical and ongoing differences in attitudes towards dingo conservation affect where and how often dingo observations are recorded in the Atlas of Living Australia (ALA).

Summarise observations

Let’s start by finding how many observations of dingoes are in the ALA and where they are.

Left: Canis familiaris (Michael Hains CC-BY-NC 3.0 (Au), Middle: Canis familiaris (GibberGavin CC-BY-NC 4.0 (Int)), Right: Canis familiaris (Danial Moore CC-BY-NC 3.0 (Au))

# packages
library(galah)
library(tidyverse)
library(ozmaps)
library(sf)
library(patchwork)
library(here)
library(rmapshaper)
library(ggpointdensity)
library(glue)
library(ggtext)

Find fields to filter data

We’ll use the {galah} package to download our data. Because Canis familiaris is the taxonomic name of both dingoes and dogs, if we only search using the scientific name, we’ll likely return more than just dingo records. To fix this, let’s filter our records to those specified by the data provider with the name common name “Dingo”.

One can imagine that sometimes data might be collected without specifying the name “Dingo” (either due to uncertainty or a lack of specificity in the survey).

If true, this means there are observations of dingoes that we’ll never know because they weren’t specified (though it’s hard to know for sure)!

Let’s use search_fields() to help us find which fields we can use to filter the taxonomic name. There are a few options, but the raw_vernacularName field seems to hold original common names specified by data providers.

search_fields("common name")

# A tibble: 4 × 4
  id                   description                                   type  link 
  <chr>                <chr>                                         <chr> <chr>
1 common_name_and_lsid Concatenation of common name and LSID, usefu… fiel… <NA> 
2 vernacularName       The common name the ALA has matched this rec… fiel… <NA> 
3 names_and_lsid       Concatenation of common name and LSID, usefu… fiel… <NA> 
4 raw_vernacularName   The original common name value supplied by t… fiel… <NA>

search_fields("raw_vernacularName") |> search_values("dingo")

# A tibble: 4 × 2
  field              category           
  <chr>              <chr>              
1 raw_vernacularName Dingo, domestic dog
2 raw_vernacularName Dingo              
3 raw_vernacularName Dingo & Dog (feral)
4 raw_vernacularName dingo

We can use the same method to find a field that contains states & territories.

search_fields("australian states")

# A tibble: 2 × 4
  id     description                                                 type  link 
  <chr>  <chr>                                                       <chr> <chr>
1 cl2013 ASGS Australian States and Territories Australian Statisti… laye… http…
2 cl22   Australian States and Territories Australian States and Te… laye… http…

search_fields("cl22") |> show_values()

# A tibble: 11 × 2
   field category                    
   <chr> <chr>                       
 1 cl22  New South Wales             
 2 cl22  Victoria                    
 3 cl22  Queensland                  
 4 cl22  South Australia             
 5 cl22  Western Australia           
 6 cl22  Northern Territory          
 7 cl22  Australian Capital Territory
 8 cl22  Tasmania                    
 9 cl22  Unknown1                    
10 cl22  Ashmore and Cartier Islands 
11 cl22  Coral Sea Islands

State observations

We’ll download the number of dingo observations in each state/territory with atlas_counts() and arrange the resulting counts in descending order.

Around 75% of dingo observations are recorded in the Northern Territory and South Australia.

dingo_counts <- galah_call() |>
  galah_identify("canis familiaris") |>
  galah_filter(raw_vernacularName == c("Dingo", "dingo")) |>
  galah_group_by(cl22) |>
  # galah_apply_profile(ALA) |> # set of data quality filters
  atlas_counts() |>
  arrange(desc(count))

cl22	count
Northern Territory	6155
South Australia	2933
Queensland	1283
New South Wales	1110
Victoria	412
Western Australia	130
Australian Capital Territory	4

Code

# Add map of australia
aus <- ozmap_data(data = "states")

# Join state counts to our map
state_counts <- aus |>
  full_join(dingo_counts, by = c("NAME" = "cl22"))

# log-transform data to scale counts for nicer plotting
logged_counts <- state_counts |>
  mutate(log = log10(count))|>
  replace_na(list(log = 0))

# plot
ggplot() +
  geom_sf(data = logged_counts,
          mapping = aes(fill = log)) +
  geom_sf_text(data = logged_counts,
               aes(label = count),
               colour = "white",
               size = 8,
               fun.geometry = sf::st_centroid) +
  scale_fill_distiller(palette = "OrRd", direction = 1) +
  labs(x = "Longitude", y = "Latitude") +
  theme_void() + 
  theme(legend.position = "none")

Data providers

Next let’s find out who the main data providers are of dingo observations to see whether observations come from citizen science programs or state monitoring programs. We’ll filter to only display providers that have provided more than 5 observations of dingoes.

data_providers <- galah_call() |>
  galah_identify("canis familiaris") |>
  galah_filter(raw_vernacularName == c("Dingo", "dingo")) |>
  galah_group_by(dataResourceName)|>
  galah_apply_profile(ALA) |>
  atlas_counts()

counts_filtered <- data_providers |>
  filter(count > 5)

# A tibble: 9 × 2
  dataResourceName                                           count
  <chr>                                                      <int>
1 Fauna Atlas N.T.                                            4867
2 SA Fauna (BDBSA)                                            2832
3 Australian National Wildlife Collection provider for OZCAM  2035
4 WildNet - Queensland Wildlife Data                          1120
5 NSW BioNet Atlas                                            1053
6 Victorian Biodiversity Atlas                                 101
7 Australian Museum provider for OZCAM                          76
8 Museums Victoria provider for OZCAM                           36
9 Northern Gulf Fauna Survey                                    18

We can also check to see where each data provider’s observations are recorded. We’ll download dingo observations using atlas_occurrences(). Then we’ll filter our observations to only those supplied by providers in counts_filtered.

You will need to first provide a registered email with the ALA using galah_config() before retrieving records.

dingo_obs <- galah_call()|>
  galah_identify("canis familiaris") |>
  galah_filter(raw_vernacularName == c("Dingo", "dingo")) |>
  galah_apply_profile(ALA) |>
  atlas_occurrences()

points_filtered <- dingo_obs |>
  filter(dataResourceName %in% counts_filtered$dataResourceName)

# A tibble: 12,138 × 8
   decimal…¹ decim…² eventDate           scien…³ taxon…⁴ recor…⁵ dataR…⁶ occur…⁷
       <dbl>   <dbl> <dttm>              <chr>   <chr>   <chr>   <chr>   <chr>  
 1     -39      146. NA                  Canis … https:… 902931… Museum… PRESENT
 2     -39      146. NA                  Canis … https:… e8fc34… Museum… PRESENT
 3     -39      146. NA                  Canis … https:… 091e59… Museum… PRESENT
 4     -38.6    143. 1866-03-26 13:55:08 Canis … https:… 13bd89… Victor… PRESENT
 5     -38.4    146. NA                  Canis … https:… 5b05ba… Museum… PRESENT
 6     -38.4    144. 1864-12-31 13:55:08 Canis … https:… c9f581… Victor… PRESENT
 7     -38.4    143. 1886-08-31 13:55:08 Canis … https:… 4cf713… Victor… PRESENT
 8     -38.4    146. NA                  Canis … https:… 8ef4ea… Museum… PRESENT
 9     -38.1    142. 1886-01-22 13:55:08 Canis … https:… 7deaea… Victor… PRESENT
10     -38.1    146. 1973-04-12 14:00:00 Canis … https:… 00a9e4… Austra… PRESENT
# … with 12,128 more rows, and abbreviated variable names ¹decimalLatitude,
#   ²decimalLongitude, ³scientificName, ⁴taxonConceptID, ⁵recordID,
#   ⁶dataResourceName, ⁷occurrenceStatus

We’ll create a bar plot and a map of observations to visualise our results.

Just five data providers account for ~98% of dingo records, with Fauna Atlas N. T. contributing ~40% of records. All major data providers are government monitoring programs, rather than citizen science providers like iNaturalist.

Code

custom_colours <- c(
  "Museums Victoria provider for OZCAM" = "#604830",
  "Victorian Biodiversity Atlas" = "#486030",
  "Australian National Wildlife Collection provider for OZCAM" = "#6090d8",
  "NSW BioNet Atlas" = "#604830",
  "WildNet - Queensland Wildlife Data" = "#6fab3f",
  "SA Fauna (BDBSA)" = "#d89060",
  "Australian Museum provider for OZCAM" = "#FFC300",
  "Fauna Atlas N.T." = "#a84830"
)

# Bar plot
counts_filtered |>
  ggplot(aes(
    x = reorder(str_wrap(dataResourceName, 28), count),
    y = count, fill = dataResourceName)) +
  geom_bar(stat = "identity", width = .8) +
  scale_fill_manual(values = custom_colours) +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_continuous(labels = scales::label_comma()) +
  coord_flip() +
  xlab("") +
  pilot::theme_pilot(grid = "v",
                     axes = "b") +
  theme(legend.position = "none",
        axis.text = element_text(size = 12))
# Map
ggplot() +
  geom_sf(data = aus, fill = "#FBFBEF") +
  geom_point(
    data = points_filtered,
    mapping = aes(
      x = decimalLongitude,
      y = decimalLatitude,
      colour = dataResourceName),
    alpha = 0.5) +
  scale_color_manual(values = custom_colours) +
  theme_void() +
  coord_sf(
    ylim = c(-45, -10),
    xlim = c(110, 155)) +
  theme(legend.position = "none")

Times of observations

Next, let’s investigate what months of the year have more dingo records. We’ll look specifically at observations in Northern Territory and South Australia because these states have the most data.

Viewing observations over time can show us patterns of when a species is most active. It can also reveal human biases in data collection. Given the huge temperature gradient between North and South Australia, we might expect there are differences between NT and SA in the timing of dingo observations.

South Australia’s dingo observations are mainly recorded during autumn months (April–June). Alternatively, Northern Territory’s dingo observations are recorded mainly over winter months (June–August).

Code

## Northern Territory

# download data
dingo_NT <- galah_call() |>
  galah_identify("canis familiaris") |>
  galah_filter(raw_vernacularName == c("Dingo", "dingo"),
               cl22 == "Northern Territory") |>
  galah_group_by(month) |>
  atlas_counts()

# format months for plotting
month_NT <- dingo_NT |>
  mutate(
    month = month(as.numeric(month), label = TRUE) # format month
    )

# plot
ggplot(data = month_NT, 
                     aes(x = month, y = count)) +
  geom_bar(stat = "identity", fill = "#a84830") +
  labs(title = "Northern Territory", 
       x = "Month", 
       y = "No. of observations") +
  scale_x_discrete(expand = c(0,0)) +
  scale_y_continuous(limits = c(0, 1000),
                     expand = c(0,0)) +
  pilot::theme_pilot(grid = "h")
## South Australia

# download data
dingo_SA <- galah_call() |>
  galah_identify("canis familiaris") |>
  galah_filter(raw_vernacularName == c("Dingo", "dingo"),
               cl22 == "South Australia") |>
  galah_group_by(month) |>
  atlas_counts()

# format months for plotting
month_SA <- dingo_SA |>
  mutate(
    month = month(as.numeric(month), label = TRUE) # format month
    )

# plot
ggplot(data = month_SA, 
                     aes(x = month, y = count)) +
  geom_bar(stat = "identity", fill = "#d89060") +
  labs(title = "South Australia", 
       x = "Month", 
       y = "No. of observations") +
  scale_x_discrete(expand = c(0,0)) +
  scale_y_continuous(limits = c(0, 1000),
                     expand = c(0,0)) +
  pilot::theme_pilot(grid = "h")

Does this suggest dingoes are more active during winter, or are data collectors surveying during cooler times of the year? The answer might be both. One dingo tracking research study found that dingoes are [far more active in winter: 46% of their day in winter was spent stationary compared to 91% in summer. This finding suggests that cooler winter temperatures allow dingoes to be more active in winter, and it’s easier for data collectors to survey during these times.

Dingo observations in Protected Areas

Differences in legislation

Why are there so many records in only two states/territories? Is it possible that differences between states/territories attitudes towards dingoes affect dingo data?

For example, the Northern Territory is the only state in Australia that recognizes dingoes as a protected species. Other states like Western Australia, New South Wales, Queensland and South Australia consider dingoes an unprotected native animal outside of the Dingo Fence, and a pest species inside the Dingo Fence (under the National Parks and Wildlife Act 1972).

Differing state legislation might help to explain why the highest numbers of observations are in states/territories that recognise them as a protected species.

Final Thoughts

Habitat loss combined with historical and current control programs have reduced total dingo numbers. Dingoes are currently listed as vulnerable to extinction under the IUNC’s Red List of Threatened Species.

Government monitoring programs provide regular and reliable data. However, incidental observations from citizen scientists can fill gaps in species distribution data.

even despite evidence that dingoes can be allies for livestock producers

Dingoes might not always benefit mammal species richness

Expand for session info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31 ucrt)
 os       Windows 10 x64 (build 19044)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_Australia.utf8
 ctype    English_Australia.utf8
 tz       Australia/Sydney
 date     2023-04-12
 pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package        * version date (UTC) lib source
 dplyr          * 1.1.0   2023-01-29 [1] CRAN (R 4.2.2)
 forcats        * 1.0.0   2023-01-29 [1] CRAN (R 4.2.2)
 galah          * 1.5.2   2023-03-20 [1] Github (AtlasOfLivingAustralia/galah@1b35520)
 ggplot2        * 3.4.1   2023-02-10 [1] CRAN (R 4.2.2)
 ggpointdensity * 0.1.0   2019-08-28 [1] CRAN (R 4.2.1)
 ggtext         * 0.1.2   2022-09-16 [1] CRAN (R 4.2.2)
 glue           * 1.6.2   2022-02-24 [1] CRAN (R 4.2.1)
 here           * 1.0.1   2020-12-13 [1] CRAN (R 4.2.1)
 htmltools      * 0.5.4   2022-12-07 [1] CRAN (R 4.2.2)
 lubridate      * 1.9.2   2023-02-10 [1] CRAN (R 4.2.2)
 ozmaps         * 0.4.5   2021-08-03 [1] CRAN (R 4.2.1)
 patchwork      * 1.1.2   2022-08-19 [1] CRAN (R 4.2.1)
 purrr          * 1.0.1   2023-01-10 [1] CRAN (R 4.2.2)
 readr          * 2.1.4   2023-02-10 [1] CRAN (R 4.2.2)
 rmapshaper     * 0.4.6   2022-05-10 [1] CRAN (R 4.2.1)
 sessioninfo    * 1.2.2   2021-12-06 [1] CRAN (R 4.2.1)
 sf             * 1.0-9   2022-11-08 [1] CRAN (R 4.2.2)
 stringr        * 1.5.0   2022-12-02 [1] CRAN (R 4.2.2)
 tibble         * 3.1.8   2022-07-22 [1] CRAN (R 4.2.1)
 tidyr          * 1.3.0   2023-01-24 [1] CRAN (R 4.2.2)
 tidyverse      * 2.0.0   2023-02-22 [1] CRAN (R 4.2.2)

 [1] C:/Users/KEL329/R-packages
 [2] C:/Users/KEL329/AppData/Local/Programs/R/R-4.2.2/library

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